Publications

2024

1. 

   Reinforcement Learning-Based Model Matching in COBRA, a Slithering Snake Robot

   Harin Kumar Nallaguntla

   Apr 2024

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   This work employs a reinforcement learning-based model identification method aimed at enhancing the accuracy of the dynamics for our snake robot, called COBRA. Leveraging gradient information and iterative optimization, the proposed approach refines the parameters of COBRA’s dynamical model such as coefficient of friction and actuator parameters using experimental and simulated data. Experimental validation on the hardware platform demonstrates the efficacy of the proposed approach, highlighting its potential to address sim-to-real gap in robot implementation.

2. AIM

   

   Non-impulsive Contact-Implicit Motion Planning for Morpho-functional Loco-manipulation

   Harin Kumar Nallaguntla, Adarsh Salagame, Kruthika Gangaraju, and 3 more authors

   Feb 2024

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   Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. The following work presents an optimization approach to solving the loco-manipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA. We present the mathematical framework and show high fidelity simulation results for fixed-shape lateral rolling trajectories that demonstrate the object manipulation.

3. 

   Loco-Manipulation with Nonimpulsive Contact-Implicit Planning in a Slithering Robot

   Harin Kumar Nallaguntla, Adarsh Salagame, Kruthika Gangaraju, and 3 more authors

   Mar 2024

   Abs HTML

   Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. The following work presents an optimization approach to solving the loco-manipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA. We present the mathematical framework and show high-fidelity simulation results and experiments to demonstrate the effectiveness of our approach.